Biochar versus hydrochar as growth media constituents for ornamental plant cultivation

[EN] Biochar and hydrochar have been proposed as novel materials for providing soilless growth media. However, much more knowledge is required before reliable advice can be given on the use of these materials for this purpose. Depending on the material and the technology applied (pyrolysis or hydrothermal carbonization), phytotoxicity and greenhouse gas emissions have been found for certain chars. In this study, our aim was to assess the feasibility of three chars as substrate constituents. We compared two biochars, one from forest waste and the other from olive mill waste, and a hydrochar from forest waste. We studied how chars affected substrate characteristics, plant performance, water economy and respiratory CO2 emission. Substrates containing biochar from forest waste showed the best characteristics, with good air/water relationships and adequate electrical conductivity. Those with biochar from olive mill waste were highly saline and, consequently, low quality. The substrates with hydrochar retained too much water and were poorly aerated, presenting high CO2 concentrations due to high respiratory activity. Plants performed well only when grown in substrates containing a maximum of 25 % biochar from forest waste or hydrochar. After analyzing the char characteristics, we concluded that biochar from forest waste could be safely used as a substrate constituent and is environmentally friendly when applied due to its low salinity and low CO2 emission. However, biochar from olive mill waste and hydrochar need to be improved before they can be used as substrate constituents.This study was funded by the Polytechnic University of Valencia (Projects on New Multidisciplinary Research; PAID-05-12). We thank Molly Marcus-McBride for supervising the English.Fornes Sebastiá, F.; Belda Navarro, RM. (2018). Biochar versus hydrochar as growth media constituents for ornamental plant cultivation. Scientia Agricola (Online). 75(4):304-312. https://doi.org/10.1590/1678-992X-2017-0062S304312754Abad, M., Noguera, P., & Burés, S. (2001). National inventory of organic wastes for use as growing media for ornamental potted plant production: case study in Spain. Bioresource Technology, 77(2), 197-200. doi:10.1016/s0960-8524(00)00152-8Bargmann, I., Martens, R., Rillig, M. C., Kruse, A., & Kücke, M. (2013). Hydrochar amendment promotes microbial immobilization of mineral nitrogen. Journal of Plant Nutrition and Soil Science, 177(1), 59-67. doi:10.1002/jpln.201300154Bargmann, I., Rillig, M. C., Buss, W., Kruse, A., & Kuecke, M. (2013). Hydrochar and Biochar Effects on Germination of Spring Barley. Journal of Agronomy and Crop Science, 199(5), 360-373. doi:10.1111/jac.12024Bedussi, F., Zaccheo, P., & Crippa, L. (2015). Pattern of pore water nutrients in planted and non-planted soilless substrates as affected by the addition of biochars from wood gasification. Biology and Fertility of Soils, 51(5), 625-635. doi:10.1007/s00374-015-1011-6Belda, R. M., Lidón, A., & Fornes, F. (2016). Biochars and hydrochars as substrate constituents for soilless growth of myrtle and mastic. Industrial Crops and Products, 94, 132-142. doi:10.1016/j.indcrop.2016.08.024Costello, R. C., & Sullivan, D. M. (2013). Determining the pH Buffering Capacity of Compost Via Titration with Dilute Sulfuric Acid. Waste and Biomass Valorization, 5(3), 505-513. doi:10.1007/s12649-013-9279-yFernandes, C., & Corá, J. E. (2004). Bulk density and relationship air/water of horticultural substrate. Scientia Agricola, 61(4), 446-450. doi:10.1590/s0103-90162004000400015Fornes, F., Belda, R. M., Carrión, C., Noguera, V., García-Agustín, P., & Abad, M. (2007). Pre-conditioning ornamental plants to drought by means of saline water irrigation as related to salinity tolerance. Scientia Horticulturae, 113(1), 52-59. doi:10.1016/j.scienta.2007.01.008Fornes, F., Belda, R. M., & Lidón, A. (2015). Analysis of two biochars and one hydrochar from different feedstock: focus set on environmental, nutritional and horticultural considerations. Journal of Cleaner Production, 86, 40-48. doi:10.1016/j.jclepro.2014.08.057Fornes, F., & Belda, R. M. (2017). Acidification with nitric acid improves chemical characteristics and reduces phytotoxicity of alkaline chars. Journal of Environmental Management, 191, 237-243. doi:10.1016/j.jenvman.2017.01.026Fornes, F., Belda, R. M., Fernández de Córdova, P., & Cebolla-Cornejo, J. (2017). Assessment of biochar and hydrochar as minor to major constituents of growing media for containerized tomato production. Journal of the Science of Food and Agriculture, 97(11), 3675-3684. doi:10.1002/jsfa.8227Fornes, F., Carrión, C., García-de-la-Fuente, R., Puchades, R., & Abad, M. (2010). Leaching composted lignocellulosic wastes to prepare container media: Feasibility and environmental concerns. Journal of Environmental Management, 91(8), 1747-1755. doi:10.1016/j.jenvman.2010.03.017GARCIADELAFUENTE, R., CARRION, C., BOTELLA, S., FORNES, F., NOGUERA, V., & ABAD, M. (2007). Biological oxidation of elemental sulphur added to three composts from different feedstocks to reduce their pH for horticultural purposes. Bioresource Technology, 98(18), 3561-3569. doi:10.1016/j.biortech.2006.11.008Genty, B., Briantais, J.-M., & Baker, N. R. (1989). The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochimica et Biophysica Acta (BBA) - General Subjects, 990(1), 87-92. doi:10.1016/s0304-4165(89)80016-9Hoitink, H. A. J., Stone, A. G., & Han, D. Y. (1997). Suppression of Plant Diseases by Composts. HortScience, 32(2), 184-187. doi:10.21273/hortsci.32.2.184Libra, J. A., Ro, K. S., Kammann, C., Funke, A., Berge, N. D., Neubauer, Y., … Emmerich, K.-H. (2011). Hydrothermal carbonization of biomass residuals: a comparative review of the chemistry, processes and applications of wet and dry pyrolysis. Biofuels, 2(1), 71-106. doi:10.4155/bfs.10.81Mazuela, P., Salas, M. del C., & Urrestarazu, M. (2005). Vegetable Waste Compost as Substrate for Melon. Communications in Soil Science and Plant Analysis, 36(11-12), 1557-1572. doi:10.1081/css-200059054Méndez, A., Paz-Ferreiro, J., Gil, E., & Gascó, G. (2015). The effect of paper sludge and biochar addition on brown peat and coir based growing media properties. Scientia Horticulturae, 193, 225-230. doi:10.1016/j.scienta.2015.07.032Nieto, A., Gascó, G., Paz-Ferreiro, J., Fernández, J. M., Plaza, C., & Méndez, A. (2016). The effect of pruning waste and biochar addition on brown peat based growing media properties. Scientia Horticulturae, 199, 142-148. doi:10.1016/j.scienta.2015.12.012Sáez, J. A., Belda, R. M., Bernal, M. P., & Fornes, F. (2016). Biochar improves agro-environmental aspects of pig slurry compost as a substrate for crops with energy and remediation uses. Industrial Crops and Products, 94, 97-106. doi:10.1016/j.indcrop.2016.08.035Smith, B. R., Fisher, P. R., & Argo, W. R. (2004). Growth and Pigment Content of Container-grown Impatiens and Petunia in Relation to Root Substrate pH and Applied Micronutrient Concentration. HortScience, 39(6), 1421-1425. doi:10.21273/hortsci.39.6.1421Solaiman, Z. M., Murphy, D. V., & Abbott, L. K. (2011). Biochars influence seed germination and early growth of seedlings. Plant and Soil, 353(1-2), 273-287. doi:10.1007/s11104-011-1031-4Steiner, C., & Harttung, T. (2014). Biochar as a growing media additive and peat substitute. Solid Earth, 5(2), 995-999. doi:10.5194/se-5-995-2014Tian, Y., Sun, X., Li, S., Wang, H., Wang, L., Cao, J., & Zhang, L. (2012). Biochar made from green waste as peat substitute in growth media for Calathea rotundifola cv. Fasciata. Scientia Horticulturae, 143, 15-18. doi:10.1016/j.scienta.2012.05.018Vaughn, S. F., Eller, F. J., Evangelista, R. L., Moser, B. R., Lee, E., Wagner, R. E., & Peterson, S. C. (2015). Evaluation of biochar-anaerobic potato digestate mixtures as renewable components of horticultural potting media. Industrial Crops and Products, 65, 467-471. doi:10.1016/j.indcrop.2014.10.04

Structure of laponite-styrene precursor dispersions for production of advanced polymer-clay nanocomposites

One method for production of polymer-clay nanocomposites involves dispersal of surface-modified clay in a polymerisable monomeric solvent, followed by fast in situ polymerisation. In order to tailor the properties of the final material we aim to control the dispersion state of the clay in the precursor solvent. Here, we study dispersions of surface-modified Laponite, a synthetic clay, in styrene via large-scale Monte-Carlo simulations and experimentally, using small angle X-ray and static light scattering. By tuning the effective interaction between simulated laponite particles we are able to reproduce the experimental scattering intensity patterns for this system, with good accuracy over a wide range of length scales. However, this agreement could only be obtained by introducing a permanent electrostatic dipole moment into the plane of each Laponite particle, which we explain in terms of the distribution of substituted metal atoms within each Laponite particle. This suggests that Laponite dispersions, and perhaps other clay suspensions, should display some of the structural characteristics of dipolar fluids. Our simulated structures show aggregation regimes ranging from networks of long chains to dense clusters of Laponite particles, and we also obtain some intriguing ‘globular’ clusters, similar to capsids. We see no indication of any ‘house-of-cards’ structures. The simulation that most closely matches experimental results indicates that gel-like networks are obtained in Laponite dispersions, which however appear optically clear and non-sedimenting over extended periods of time. This suggests it could be difficult to obtain truly isotropic equilibrium dispersion as a starting point for synthesis of advanced polymer-clay nanocomposites with controlled structures

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release

Real-world assessment and characteristics of men with benign prostatic hyperplasia (BPH) in primary care and urology clinics in Spain

Objectives: To describe the real-world demographic and clinical characteristics of patients with lower urinary tract symptoms (LUTS) as a result of benign prostatic hyperplasia (BPH) in Spain. Methodology: This observational, retrospective, multicentre study conducted in primary care and urology clinics in Spain included men aged ≥50 years diagnosed (≤8 years prior to study visit) with LUTS caused by BPH. The primary endpoint was demographic and clinical characteristics; secondary endpoints included disease progression and diagnostic tests across both healthcare settings. Results: A total of 670 patients were included (primary care: n = 435; urology: n = 235). Most patients had moderate/severe LUTS (74.6%) and prostate volume >30 cc (81.7%), with no differences between settings. More patients had prostate-specific antigen (PSA) ≥1.5 ng/mL in primary care (74.5%) versus urology (67.7%). Progression criteria were prevalent (48.9%). Clinical criteria were more commonly used than the International Prostate Symptom Score (IPSS) to evaluate LUTS at diagnosis (primary care: clinical criteria 73.0%; IPSS: 26.9%; urology: clinical criteria 76.5%; IPSS: 23.4%). Proportion of patients with moderate/severe LUTS at diagnosis was lower using clinical criteria than IPSS, and the proportion of patients with 'worsening' LUTS (diagnosis to study visit) was higher when using clinical criteria versus IPSS. In both healthcare settings, the most commonly used diagnostic tests were general and urological clinical history and PSA. Conclusion: Demographic and clinical characteristics of patients with BPH in Spain were similar in primary care and urology; however, assessment criteria to evaluate LUTS severity differ and are not completely aligned with clinical guideline recommendations. Increased use of recommended assessments may enhance optimal BPH management

Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. In: Proceedings of the sixth international joint conference on autonomous agents and multi-agent systems, pp 172–179Abdu H, Lutfiyya H, Bauer MA (1999) A model for adaptive monitoring configurations. In: Proceedings of the VI IFIP/IEEE IM conference on network management, pp 371–384Alberola JM, Julian V, Garcia-Fornes A (2011) A cost-based transition approach for multiagent systems reorganization. In: Proceedings of the 10th international conference on aut. agents and MAS (AAMAS11), pp 1221–1222Alberola JM, Julian V, Garcia-Fornes A (2012) Multi-dimensional transition deliberation for organization adaptation in multiagent systems. In: Proceedings of the 11th international conference on aut. agents and MAS (AAMAS12) (in press)Argente E, Julian V, Botti V (2006) Multi-agent system development based on organizations. Electron Notes Theor Comput Sci 160(3):55–71Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the Thomas approach. Knowl Inf Syst 29(2):379–403Ashford SJ, Taylor MS (1990) Adaptation to work transitions. An integrative approach. Res Pers Hum Resour Manag 8:1–39Ashford SJ, Blatt R, Walle DV (2003) Reflections on the looking glass: a review of research on feedback-seeking behavior in organizations. J Manag 29(6):773–799Astley WG, Van de Ven AH (1983) Central perspectives and debates in organization theory. Adm Sci Q 28(2):245–273Bond AH, Gasser L (1988) A survey of distributed artificial intelligence readings in distributed artificial intelligence. Morgan Kaufmann, Los AltosBou E, López-Sánchez M, Rodríguez-Aguilar JA (2006) Adaptation of autonomic electronic institutions through norms and institutional agents In: Engineering societies in the agents world. Number LNAI 445, Springer, Dublin, pp 300–319Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2007) Towards self-configuration in autonomic electronic institutions. In: COIN 2006 workshops. Number LNAI 4386, pp 220–235Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2008) Using case-based reasoning in autonomic electronic institutions. In: Proceedings of the 2007 international conference on coordination, organizations, institutions, and norms in agent systems III, pp 125–138Brett JM, Feldman DC, Weingart LR (1990) Feedback-seeking behavior of new hires and job changers. J Manag 16:737–749Bulka B, Gaston ME, desJardins M (2007) Local strategy learning in networked multi-agent team formation. Auton Agents Multi-Agent Syst 15(1):29–45Campos J, López-Sánchez M, Esteva M (2009) Assistance layer, a step forward in multi-agent systems. In: Coordination support international joint conference on autonomous agents and multiagent systems (AAMAS), pp 1301–1302Campos J, Esteva M, López-Sánchez M, Morales J, Salamó M (2011) Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing 91(2):169–215Carley KM, and Gasser L (1999) Computational organization theory. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, pp 299–330Carvalho G, Almeida H, Gatti M, Vinicius G, Paes R, Perkusich, A, Lucena C (2006) Dynamic law evolution in governance mechanisms for open multi-agent systems. In: Second workshop on software engineering for agent-oriented systemsCernuzzi L, Zambonelli F (2011) Adaptive organizational changes in agent-oriented methodologies. Knowl Eng Rev 26(2):175–190Cheng BH, Lemos R, Giese H, Inverardi P, Magee J (2009) Software engineering for self-adaptive systems: a research roadmap, pp 1–26Corkill DD, Lesser VR (1983) The use of meta-level control for coordination in a distributed problem solving networks. In: Proceedings of the eighth international joint conference on artificial intelligence. IEEE Computer Society Press, pp 748–756Corkill DD, Lander SE (1998) Diversity in agent organizations. Object Mag 8(4):41–47de Paz JF, Bajo J, González A, Rodríguez S, Corchado JM (2012) Combining case-based reasoning systems and support vector regression to evaluate the atmosphere-ocean interaction. Knowl Inf Syst 30(1):155–177DeLoach SA, Matson E (2004) An organizational model for designing adaptive multiagent systems. In: The AAAI-04 workshop on agent organizations: theory and practice (AOTP), pp 66–73DeLoach SA, Oyeman W, Matson E (2008) A capabilities-based model for adaptive organizations. Auton Agents Multi-Agent Syst 16:13–56Dignum V, Dignum F (2001) Modelling agent societies: co-ordination frameworks and institutions progress in artificial intelligence. LNAI 2258, pp 191–204Dignum V (2004) A model for organizational interaction: based on agents, founded in logic. PhD dissertation, Universiteit Utrecht. SIKS dissertation series 2004-1Dignum V, Dignum F, Sonenberg L (2004) Towards dynamic reorganization of agent societies. In: Proceedings of the workshop on coordination in emergent agent societies, pp 22–27Dignum V, Dignum F (2006) Exploring congruence between organizational structure and task performance: a simulation approach coordination, organization, institutions and norms in agent systems I. In: Proceedings of the ANIREM ’05/OOOP ’05, pp 213–230Dignum V, Dignum F (2007) A logic for agent organizations. In: Proceedings of the multi-agent logics, languages, and organisations federated workshops (MALLOW ’007), formal approaches to multi-agent systems (FAMAS ’007) workshopFox MS (1981) Formalizing virtual organizations. IEEE Transact Syst Man Cybern 11(1):70–80Gaston ME, desJardins M (2005) Agent-organized networks for dynamic team formation. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems, pp 230–237Gaston ME, desJardins M (2008) The effect of network structure on dynamic team formation in multi-agent systems. Comput Intell 24(2):122–157Norbert G, Philippe M (1997) The reorganization of societies of autonomous agents. In: MAAMAW-97. Springer, London, pp 98–111Goldman CV, Rosenschein JS (1997) Evolving organizations of agents American association for artificial intelligence. In: Multiagent learning workshop at AAAI97Greve HR (1998) Performance, aspirations, and risky organizational change. Adm Sci Quart 43(1):58–86Guessoum Z, Ziane M, Faci N (2004) Monitoring and organizational-level adaptation of multi-agent systems. In: Proceedings of the AAMAS ’04, pp 514–521Hoogendoorn M, Treur J (2006) An adaptive multi-agent organization model based on dynamic role allocation. In: Proceedings of the IAT ’06, pp 474–481Horling B, Benyo B, Lesser V (1999) Using self-diagnosis to adapt organizational structures. In: Proceedings of the 5th international conference on autonomous agents, pp 529–536Horling B, Lesser V (2005) A survey of multi-agent organizational paradigms. Knowl Eng Rev 19(4): 281–316Hrebiniak LG, Joyce WF (1985) Organizational adaptation: strategic choice and environmental determinism. Adm Sci Quart 30(3):336–349Hübner JF, Sichman JS, Boissier O (2002) MOISE+: towards a structural, functional, and deontic model for MAS organization. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems, pp 501–502Hübner JF, Sichman JS, Boissier O (2004) Using the MOISE+ for a cooperative framework of MAS reorganisation. In: Proceedings of the 17th Brazilian symposium on artificial intelligence (SBIA ’04), vol 3171, pp 506–515Hübner JF, Boissier O, Sichman JS (2005) Specifying E-alliance contract dynamics through the MOISE + reorganisation process Anais do V Encontro Nacional de Inteligde Inteligncia Artificial (ENIA 2005)Jennings NR (2001) An agent-based approach for building complex software systems. Commun ACM 44(4):35–41Kamboj S, Decker KS (2006) Organizational self-design in semi-dynamic environments In: 2007 IJCAI workshop on agent organizations: models and simulations (AOMS@IJCAI), pp 335–337Katz D, Kahn RL (1966) The social psychology of organizations. Wiley, New YorkKelly D, Amburgey TL (1991) Organizational inertia and momentum: a dynamic model of strategic change. Acad Manag J 34(3):591–612Kephart J, Chess DM (2003) The vision of autonomic computing. Computer 36(1):41–50Kim DH (1993) The link between individual and organizational learning. Sloan Manag Rev 35(1):37–50Kota R, Gibbins N, Jennings NR (2009a) Decentralised structural adaptation in agent organisations organized adaptation in multi-agent systems, pp 54–71Kota R, Gibbins N, Jennings NR (2009b) Self-organising agent organisations. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems (AAMAS 2009)Kota R, Gibbins N, Jennings NR (2012) Decentralised approaches for self-adaptation in agent organisations. ACM Trans Auton Adapt Syst 7(1):1–28Kotter J, Schlesinger L (1979) Choosing strategies for change. Harv Bus Rev 106–1145Lesser VR (1998) Reflections on the nature of multi-agent coordination and its implications for an agent architecture. Auton Agents Multi-Agent Syst 89–111Levitt B, March JG (1988) Organizational learning. Annu Rev Sociol 14:319–340Luck M, McBurney P, Shehory O, Willmott S (2005) Agent technology: computing as interaction (a roadmap for agent based computing)Mathieu P, Routier JC, Secq Y (2002a) Dynamic organization of multi-agent systems. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1, pp 451–452Mathieu P, Routier JC, Secq Y (2002b) Principles for dynamic multi-agent organizations. In: Proceedings of the 5th Pacific rim international workshop on multi agents: intelligent agents and multi-agent systems, pp 109–122Matson E, DeLoach S (2003) Using dynamic capability evaluation to organize a team of cooperative, autonomous robots. In: Proceedings of the 2003 international conference on artificial intelligence (IC-AI ’03), Las Vegas, pp 23–26Matson E, DeLoach S (2004) Enabling intra-robotic capabilities adaptation using an organization-based multiagent system. ICRA, pp 2135–2140Matson E, DeLoach S (2005) Formal transition in agent organizations. In: IEEE international conference on knowledge intensive multiagent systems (KIMAS ’05)Matson E, Bhatnagar R (2006) Properties of capability based agent organization transition. In: Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology IAT ’06, pp 59–65Morales J, López-Sánchez M, Esteva, M (2011) Using experience to generate new regulations. In: Proceedings of the twenty-second international joint conference on artificial Intelligence (IJCAI-11), pp 307–312Muhlestein D, Lim S (2011) Online learning with social computing based interest sharing. Knowl Inf Syst 26(1):31–58Nair R, Tambe M, Marsella S (2003) Role allocation and reallocation in multiagent teams: towards a practical analysis. In: Proceedings of the second AAMAS ’03, pp 552–559Orlikowski WJ (1996) Improvising organizational transformation over time: a situated change perspective. Inf Syst Res 7(1):63–92Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11:387–434Ringold PL, Alegria J, Czaplewski RL, Mulder BS, Tolle T, Burnett K (1996) Adaptive monitoring design for ecosystem management. Ecol Appl 6(3):745–747Routier J, Mathieu P, Secq Y (2001) Dynamic skill learning: a support to agent evolution. In: Proceedings of the artificial intelligence and the simulation of behaviour symposium on adaptive agents and multi-agent systems (AISB ’01), pp 25–32Scott RW (2002) Organizations: rational, natural, and open systems, 5th edn. Prentice Hall International, New YorkSeelam A (2009) Reorganization of massive multiagent systems: MOTL/O http://books.google.es/books?id=R-s8cgAACAAJ . Southern Illinois University CarbondaleSo Y, Durfee EH (1993) An organizational self-design model for organizational change. In: AAAI93 workshop on AI and theories of groups and oranizations, pp 8–15So Y, Durfee EH (1998) Designing organizations for computational agents. Simulating organizations. MIT Press, Cambridge, pp 47–64Schwaninger M (2000) A theory for optimal organization. Technical report. Institute of Management at the University of St. Gallen, SwitzerlandTantipathananandh C, Berger-Wolf TY (2011) Finding communities in dynamic social networks. In: IEEE 11th international conference on data mining 2011, pp 1236–1241Wang Z, Liang X (2006) A graph based simulation of reorganization in multi-agent systems. In: IEEE WICACM international conference on intelligent agent technology, pp 129–132Wang D, Tse Q, Zhou Y (2011) A decentralized search engine for dynamic web communities. Knowl Inf Syst 26(1):105–125Weick KE (1979) The social psychology of organizing, 2nd edn. Addison-Wesley, ReadingWeyns D, Haesevoets R, Helleboogh A, Holvoet T, Joosen W (2010a) The MACODO middleware for context-driven dynamic agent organizations. ACM Transact Auton Adapt Syst 3:1–3:28Weyns D, Malek S, Andersson J (2010b) FORMS: a formal reference model for self-adaptation. In: Proceedings of the 7th international conference on autonomic computing, pp 205–214Weyns D, Georgeff M (2010) Self-adaptation using multiagent systems. IEEE Softw 27(1):86–91Zhong C (2006) An investigation of reorganization algorithms. Master-thesi

Erratum: JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package

An Open Source Simulation Model for Soil and Sediment Bioturbation

Bioturbation is one of the most widespread forms of ecological engineering and has significant implications for the structure and functioning of ecosystems, yet our understanding of the processes involved in biotic mixing remains incomplete. One reason is that, despite their value and utility, most mathematical models currently applied to bioturbation data tend to neglect aspects of the natural complexity of bioturbation in favour of mathematical simplicity. At the same time, the abstract nature of these approaches limits the application of such models to a limited range of users. Here, we contend that a movement towards process-based modelling can improve both the representation of the mechanistic basis of bioturbation and the intuitiveness of modelling approaches. In support of this initiative, we present an open source modelling framework that explicitly simulates particle displacement and a worked example to facilitate application and further development. The framework combines the advantages of rule-based lattice models with the application of parameterisable probability density functions to generate mixing on the lattice. Model parameters can be fitted by experimental data and describe particle displacement at the spatial and temporal scales at which bioturbation data is routinely collected. By using the same model structure across species, but generating species-specific parameters, a generic understanding of species-specific bioturbation behaviour can be achieved. An application to a case study and comparison with a commonly used model attest the predictive power of the approach